Proceedings of the 1st ACM workshop on Wireless multimedia networking and performance modeling
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 1st ACM International Workshop on Wireless Multimedia Networking and Performance Modeling - WMuNeP'05. The demand for wireless multimedia communications thrives in today's consumer and corporate market. The need to evolve multimedia applications and services, and their associated protocols for emerging networks is at a critical point given the proliferation and integration of wireless systems to intelligent and broadband networks, mobility of people, data/voice convergence and the integration of computing and communication in mobile devices. The goal of this workshop is to cover different aspects of wireless multimedia networking and performance modeling for WLANs, WPANs, WMANs, WWANs, MANETs and sensor networks such as wireless video and wireless streaming, systematic design methodologies, algorithms, synchronization, analysis and performance modeling. We hope that this workshop will provide a forum for researchers and practitioners to present their contributions related to the above high-level aspect.The call for papers attracted 41 submissions from countries in Asia, Europe, North America and South America. The program committee accepted 18 papers that cover a variety of topics on wireless multimedia and performance modeling. We hope that these proceedings will serve as a valuable reference for researchers and developers in this important community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".